# Make random matrices for speed testing
import scipy.io
import numpy as np
from generalize_ising_model.core import generalized_ising
import time
import matplotlib.pyplot as plt
import collections
from generalize_ising_model.ising_utils import distance_wei, to_normalize, to_save_results, makedir, to_generate_randon_graph, save_graph
from generalize_ising_model.phi_project.utils import to_estimate_tpm_from_ising_model, to_calculate_mean_phi, to_save_phi
dir_output_name = '/home/user/Desktop/phiTest/speedTest'
size = int(input('Matrix size?'))
output_path = dir_output_name + '/' + 'phi/' + str(size) + '/'
makedir(output_path)
Jij = save_graph(output_path + 'Jij_' + str(size) + '.csv',
to_generate_randon_graph(size, isolate=False, weighted=True))
# Ising Parameters
temperature_parameters = (
-1, 5, 50
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = 100 # Number of simulation after thermalization
thermalize_time = 0.3 #
makedir(dir_output_name)
#i_l = []
#j_l = []
# 'VIM': [4, 20, 12, 9, 73],
# 'VIO': [20, 10, 12, 4, 6]}
d = collections.OrderedDict(sorted(rsn_index.items()))
Jij = Jij['meanJ_prob']
D, B = distance_wei(1. / Jij)
# Ising Parameters
temperature_parameters = (
-1, 5, 500
) # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = 500 # Number of simulation after thermalization
thermalize_time = 0.3 #
makedir(dir_output_name)
for key, value in d.items():
i_l = []
j_l = []
for v in value:
for w in value:
i_l.append(v - 1)
j_l.append(w - 1)
J = 1. / D[i_l, j_l]
J[J == np.inf] = 0
J[J == -np.inf] = 0
J = J / np.max(J)
J = np.reshape(J, (len(value), len(value)))
import numpy as np
from networkx.utils import *
import os
from generalize_ising_model.ising_utils import save_graph, makedir
path_input = '/home/brainlab/Desktop/Rudas/Data/Ising/'
simulation_name = 'experiment_2'
default_size = 40
default_no_entities = 5
path_output_data = path_input + simulation_name
makedir(path_input + simulation_name)
# Degree
path_outputexp_degree = path_output_data + '/degree/'
if not os.path.exists(path_outputexp_degree):
os.mkdir(path_outputexp_degree)
degrees_expected = np.linspace(5, 35, num=20).astype(np.int8)
for degree in degrees_expected:
path_output_data_degree = path_outputexp_degree + 'degree_' + str(degree)
if not os.path.exists(path_output_data_degree):
os.mkdir(path_output_data_degree)
for entity in range(default_no_entities):
path_output_data_degree_entity = path_output_data_degree + '/' + 'entity_' + str(entity)
if not os.path.exists(path_output_data_degree_entity):
os.mkdir(path_output_data_degree_entity)
def phi_sim_save(size, output_directory,count,temperature_parameters=(-1,5,50), no_simulations=500, thermalize_time=0.3):
import numpy as np
from generalize_ising_model.core import generalized_ising
import time
from generalize_ising_model.ising_utils import to_save_results, makedir, \
to_generate_randon_graph, save_graph
from generalize_ising_model.phi_project.utils import to_estimate_tpm_from_ising_model, to_calculate_mean_phi, \
to_save_phi
makedir(output_directory + '/phi/')
makedir(output_directory + '/ising/')
size = int(size)
output_path_phi = output_directory + '/' + 'phi/' + str(count) + '/'
output_path_ising = output_directory + '/' + 'ising/' + str(count) + '/'
if makedir(output_path_ising) and makedir(output_path_phi):
J = save_graph(output_path_phi + 'Jij_' + str(count) + '.csv',
to_generate_randon_graph(size, isolate=False, weighted=True))
# Ising Parameters
temperature_parameters = temperature_parameters # Temperature parameters (initial tempeture, final tempeture, number of steps)
no_simulations = no_simulations # Number of simulation after thermalization
thermalize_time = thermalize_time #
start_time = time.time()
print('Fitting Generalized Ising model for a ', size, ' by', size, ' random matrix.')
simulated_fc, critical_temperature, E, M, S, H, spin_mean = generalized_ising(J,
temperature_parameters=temperature_parameters,
no_simulations=no_simulations,
thermalize_time=thermalize_time,
temperature_distribution='log')
to_save_results(temperature_parameters, J, E, M, S, H, simulated_fc, critical_temperature, output_path_ising,
temperature_distribution='log')
print('It took ', time.time() - start_time, 'seconds to fit the generalized ising model')
print('Computing Phi for random ' + str(size) + ' by ' + str(size) + ' matrix')
ts = np.logspace(temperature_parameters[0], np.log10(temperature_parameters[1]), temperature_parameters[2])
phi_temperature, phi_sum, phi_sus = [], [], []
cont = 0
start_time = time.time()
for t in ts:
# print('Temperature: ' + str(t))
tpm, fpm = to_estimate_tpm_from_ising_model(J, t)
phi_, phiSum, phiSus = to_calculate_mean_phi(fpm, spin_mean[:, cont], t)
phi_temperature.append(phi_)
phi_sum.append(phiSum)
phi_sus.append(phiSus)
cont += 1
to_save_phi(ts, phi_temperature, phi_sum, phi_sus, S, critical_temperature, size, output_path_phi)
print('It takes ', time.time() - start_time, 'seconds to compute phi for a ', size, 'by', size,
' random matrix.')
else:
print(str(count) + ' is already done!')
from generalize_ising_model.ising_utils import save_graph, makedir
import networkx as nx
default_size = 40
default_no_entities = 20
dim = 3
path_input = '/home/brainlab/Desktop/Rudas/Data/Ising/experiment_2/'
path_output_data = path_input + 'geometrics/'
makedir(path_output_data)
#Random geometric graph
path_output_data_type = path_output_data + 'geometricxxx_' + str(dim) + '/'
makedir(path_output_data_type)
for i in range(default_no_entities):
while True:
G = nx.random_geometric_graph(default_size, 0.2, dim=dim)
if len(list(nx.isolates(G))) == 0:
break
makedir(path_output_data_type + 'entity_' + str(i))
save_graph(path_output_data_type + 'entity_' + str(i) + '/' + 'J_ij.csv',
G)
from natsort import natsorted
from generalize_ising_model.ising_utils import makedir
from dipy.io.streamline import save_trk, load_trk
import nibabel as nib
from dipy.tracking.utils import connectivity_matrix
import matplotlib
import numpy as np
from nilearn import plotting
parcellations_path = '/home/brainlab/Desktop/rsn_parcellations/'
subjects_path = '/home/brainlab/Desktop/Rudas/Data/testkokokoko/subjects/'
output_path = '/home/brainlab/Desktop/Rudas/Data/testkokokoko/output/'
for subject in natsorted(os.listdir(subjects_path)):
print(subject)
makedir(output_path + subject)
trk_file = nib.streamlines.load(subjects_path + subject + '/' + 'tractography.trk', lazy_load=True)
for network in natsorted(os.listdir(parcellations_path)):
print(network)
makedir(output_path + subject + '/' + network)
for size in natsorted(os.listdir(parcellations_path + network)):
print(size)
makedir(output_path + subject + '/' + network + '/' + size)
parcellation_img = nib.load(parcellations_path + network + '/' + size)
parcellation_data = parcellation_img.get_data()
parcellation_affine = parcellation_img.affine
M, grouping = connectivity_matrix(trk_file.streamlines, parcellation_data, affine=trk_file.affine,
return_mapping=True,
mapping_as_streamlines=True)
thermalize_time=thermalize_time,
external_field=external_field_sin_all[:, i_ef])
f.add_subplot(2, 2, 1)
plt.plot(ts, E1, marker='o', color=colors[i_ef])
plt.xlabel("Temperature (T)", fontsize=20)
plt.ylabel("Energy ", fontsize=20)
plt.axis('tight')
f.add_subplot(2, 2, 2)
plt.plot(ts, abs(M1), marker='o', color=colors[i_ef])
plt.xlabel("Temperature (T)", fontsize=20)
plt.ylabel("Magnetization ", fontsize=20)
plt.axis('tight')
f.add_subplot(2, 2, 3)
plt.plot(ts, H1, marker='o', color=colors[i_ef])
plt.xlabel("Temperature (T)", fontsize=20)
plt.ylabel("Specific Heat", fontsize=20)
plt.axis('tight')
f.add_subplot(2, 2, 4)
plt.plot(ts, S1, marker='o', color=colors[i_ef])
plt.xlabel("Temperature (T)", fontsize=20)
plt.ylabel("Susceptibility", fontsize=20)
plt.axis('tight')
plt.show()
makedir(path_output)
save_results(temperature_parameters, J, E, M, S, H, simulated_fc,
critical_temperature, path_output)
